Class ChatAgent

Agent for the MRKL chain.

⚠️ Deprecated ⚠️

Use the createStructuredChatAgent method instead.

This feature is deprecated and will be removed in the future.

It is not recommended for use.

Hierarchy (view full)

Agent
- ChatAgent

Index

Constructors

constructor

new ChatAgent(input): ChatAgent
Parameters
- input: ChatAgentInput
Returns ChatAgent
Overrides Agent.constructor
- Defined in langchain/src/agents/chat/index.ts:58

Properties

ToolType

ToolType: ToolInterface

llmChain

llmChain: LLMChain<string, LLMType>

outputParser

outputParser: undefined | AgentActionOutputParser

Accessors

allowedTools

get allowedTools(): undefined | string[]
Returns undefined | string[]
Inherited from Agent.allowedTools
- Defined in langchain/src/agents/agent.ts:491

inputKeys

get inputKeys(): string[]
Returns string[]
Inherited from Agent.inputKeys
- Defined in langchain/src/agents/agent.ts:495

returnValues

get returnValues(): string[]
Returns string[]
Inherited from Agent.returnValues
- Defined in langchain/src/agents/agent.ts:57

Methods

constructScratchPad

constructScratchPad(steps): Promise<string>
Constructs the agent's scratchpad, which is a string representation of the agent's previous steps.
Parameters
- steps: AgentStep[]
  Array of AgentStep instances representing the agent's previous steps.
Returns Promise<string>
Promise resolving to a string representing the agent's scratchpad.
Overrides Agent.constructScratchPad
- Defined in langchain/src/agents/chat/index.ts:111

finishToolName

finishToolName(): string
Name of tool to use to terminate the chain.

Returns string
Inherited from Agent.finishToolName
- Defined in langchain/src/agents/agent.ts:569

llmPrefix

llmPrefix(): string
Prefix to append the LLM call with.

Returns string
Overrides Agent.llmPrefix
- Defined in langchain/src/agents/chat/index.ts:72

observationPrefix

observationPrefix(): string
Prefix to append the observation with.

Returns string
Overrides Agent.observationPrefix
- Defined in langchain/src/agents/chat/index.ts:68

plan

plan(steps, inputs, callbackManager?): Promise<AgentAction | AgentFinish>
Decide what to do given some input.
Parameters
- steps: AgentStep[]
  Steps the LLM has taken so far, along with observations from each.
- inputs: ChainValues
  User inputs.
- Optional callbackManager: CallbackManager
  Callback manager to use for this call.
Returns Promise<AgentAction | AgentFinish>
Action specifying what tool to use.
Inherited from Agent.plan
- Defined in langchain/src/agents/agent.ts:623

prepareForOutput

prepareForOutput(_returnValues, _steps): Promise<Record<string, any>>
Prepare the agent for output, if needed
Parameters
- _returnValues: Record<string, any>
- _steps: AgentStep[]
Returns Promise<Record<string, any>>
Inherited from Agent.prepareForOutput
- Defined in langchain/src/agents/agent.ts:99

returnStoppedResponse

returnStoppedResponse(earlyStoppingMethod, steps, inputs, callbackManager?): Promise<AgentFinish>
Return response when agent has been stopped due to max iterations
Parameters
- earlyStoppingMethod: StoppingMethod
- steps: AgentStep[]
- inputs: ChainValues
- Optional callbackManager: CallbackManager
Returns Promise<AgentFinish>
Inherited from Agent.returnStoppedResponse
- Defined in langchain/src/agents/agent.ts:634

`Static` createPrompt

createPrompt(tools, args?): ChatPromptTemplate<any, any>
Create prompt in the style of the zero shot agent.
Parameters
- tools: ToolInterface[]
  List of tools the agent will have access to, used to format the prompt.
- Optional args: ChatCreatePromptArgs
  Arguments to create the prompt with.
Returns ChatPromptTemplate<any, any>
Overrides Agent.createPrompt
- Defined in langchain/src/agents/chat/index.ts:129

`Static` deserialize

deserialize(data): Promise<Agent>
Load an agent from a json-like object describing it.
Parameters
- data: SerializedZeroShotAgent & {
  llm?: BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>;
  tools?: ToolInterface[];
  }
Returns Promise<Agent>
Inherited from Agent.deserialize
- Defined in langchain/src/agents/agent.ts:676

`Static` fromLLMAndTools

fromLLMAndTools(llm, tools, args?): ChatAgent
Creates a ChatAgent instance using a language model, tools, and optional arguments.
Parameters
- llm: BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>
  BaseLanguageModelInterface instance to use in the agent.
- tools: ToolInterface[]
  Array of Tool instances to include in the agent.
- Optional args: ChatCreatePromptArgs & AgentArgs
  Optional arguments to customize the agent and prompt.
Returns ChatAgent
ChatAgent instance
Overrides Agent.fromLLMAndTools
- Defined in langchain/src/agents/chat/index.ts:157

`Static` getDefaultOutputParser

getDefaultOutputParser(_fields?): ChatAgentOutputParser
Returns a default output parser for the ChatAgent.
Parameters
- Optional _fields: OutputParserArgs
  Optional OutputParserArgs to customize the output parser.
Returns ChatAgentOutputParser
ChatAgentOutputParser instance
Overrides Agent.getDefaultOutputParser
- Defined in langchain/src/agents/chat/index.ts:101

`Static` validateTools

validateTools(tools): void
Validates that all tools have descriptions. Throws an error if a tool without a description is found.
Parameters
- tools: ToolInterface[]
  Array of Tool instances to validate.
Returns void
void
Overrides Agent.validateTools
- Defined in langchain/src/agents/chat/index.ts:86

Class ChatAgent

⚠️ Deprecated ⚠️

Hierarchy (view full)

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

Parameters

input: ChatAgentInput

Returns ChatAgent

Properties

ToolType

llmChain

outputParser

Accessors

allowedTools

Returns undefined | string[]

inputKeys

Returns string[]

returnValues

Returns string[]

Methods

constructScratchPad

Parameters

steps: AgentStep[]

Returns Promise<string>

finishToolName

Returns string

llmPrefix

Returns string

observationPrefix

Returns string

plan

Parameters

steps: AgentStep[]

inputs: ChainValues

Optional callbackManager: CallbackManager

Returns Promise<AgentAction | AgentFinish>

prepareForOutput

Parameters

_returnValues: Record<string, any>

_steps: AgentStep[]

Returns Promise<Record<string, any>>

returnStoppedResponse

Parameters

earlyStoppingMethod: StoppingMethod

steps: AgentStep[]

inputs: ChainValues

Optional callbackManager: CallbackManager

Returns Promise<AgentFinish>

Static createPrompt

Parameters

tools: ToolInterface[]

Optional args: ChatCreatePromptArgs

Returns ChatPromptTemplate<any, any>

Static deserialize

Parameters

data: SerializedZeroShotAgent & { llm?: BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>; tools?: ToolInterface[]; }

Returns Promise<Agent>

Static fromLLMAndTools

Parameters

llm: BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>

tools: ToolInterface[]

Optional args: ChatCreatePromptArgs & AgentArgs

Returns ChatAgent

Static getDefaultOutputParser

Parameters

Optional _fields: OutputParserArgs

Returns ChatAgentOutputParser

Static validateTools

Parameters

tools: ToolInterface[]

Returns void

Settings

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`Optional` callbackManager: CallbackManager

`Optional` callbackManager: CallbackManager

`Static` createPrompt

`Optional` args: ChatCreatePromptArgs

`Static` deserialize

data: SerializedZeroShotAgent & {
llm?: BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>;
tools?: ToolInterface[];
}

`Static` fromLLMAndTools

`Optional` args: ChatCreatePromptArgs & AgentArgs

`Static` getDefaultOutputParser

`Optional` _fields: OutputParserArgs

`Static` validateTools